Methods combining the feature extraction, feature learning and classification steps in an end-to-end learning process have been recently proposed for financial time-series forecasting, and have shown to outperform methodologies in which these steps are independently designed. A deep learning methodology based on Convolutional Neural Networks for prediction of mid-price changes is proposed in [1]}. A combined model based on Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) layers outpeforming individual LSTM and CNN models is proposed in [2]}. A model combining CNN layers for feature extraction and LSTM layers to capture time dependencies is proposed in [3]}, and it was shown to outperform most of existing models for mid-price prediction. The Temporal Logistic Neural Bag-of-Features model was proposed in [4]} and it was shown to perform better than the baseline models. A trainable normalization scheme which can be jointly optimized with any deep learning method for time-series classification was proposed in [5]}. The Temporal Attention-Augmented Bilinear (TABL) network was proposed in [6]} and combines bilinear projections with an attention mechanism. It was shown in [6]} that TABL network provides state-of-the-art performance while requiring a low number of parameters compared to the competing methods.
